Active image depth prediction

ABSTRACT

An active depth detection system can generate a depth map from an image and user interaction data, such as a pair of clicks. The active depth detection system can be implemented as a recurrent neural network that can receive the user interaction data as runtime inputs after training. The active depth detection system can store the generated depth map for further processing, such as image manipulation or real-world object detection.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.16/120,105, filed on Aug. 31, 2018, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to machinelearning and, more particularly, but not by way of limitation, toimage-based depth estimation.

BACKGROUND

Depth estimation schemes attempt to determine the depths of objectsdepicted in images (e.g., an image, video). The depth data can be usefulfor different image-based tasks, such as augmented reality, imagefocusing, and face parsing. Some depth detection techniques use externalsignals (e.g., infrared beams) to bounce off nearby objects to assist indetermining the depths of objects in a given image. While theseexternal-signal-based approaches can yield accurate results, many userdevices (e.g., a smartphone, a laptop) are not equipped with thenecessary hardware (e.g., infrared (IR) laser, IR camera) to enablesignal-based depth detection. Determining depths directly from a singleimage is difficult because of the inherent ambiguities between anobject's appearance in an image and its real-world geometry.

BRIEF DESCRIPTION OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to the FIG.number in which that element or act is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network.

FIG. 2 is a block diagram illustrating further details regarding themessaging system of FIG. 1, according to example embodiments.

FIG. 3 is a schematic diagram illustrating data that may be stored in adatabase of a messaging server system, according to certain exampleembodiments.

FIG. 4 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 5 is a schematic diagram illustrating an example access-limitingprocess, in terms of which access to content (e.g., an ephemeral messageand associated multimedia payload of data) or a content collection(e.g., an ephemeral message story) may be time-limited (e.g., madeephemeral), according to some example embodiments.

FIG. 6 shows example functional engines of an active depth system,according to some example embodiments.

FIG. 7 shows a flow diagram of a method for implementing an active depthmap, according to some example embodiments.

FIG. 8 shows a flow diagram of an example method for utilizing sets ofclick pair data, according to some example embodiments.

FIG. 9A shows an example network for a depth engine, according to someexample embodiments.

FIG. 9B shows an example image and depth map, according to some exampleembodiments.

FIGS. 10-13 show example user interfaces for implementing the activedepth system, according to some example embodiments.

FIG. 14 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 15 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

As discussed, determining the depths of objects depicted in an image isdifficult. While some external-signal-based approaches can assist inimage-based depth detection, many end-user systems (e.g., clientdevices, smartphones, laptops) lack the specialized equipment requiredto implement these signal-based approaches.

To this end, an active depth detection system can be implemented todetermine depths of objects directly from an image using runtimeend-user-provided data (e.g., end-user input data provided to the systemafter training). According to some approaches, the active depthdetection system includes one or more neural networks that areconfigured to generate a depth map and refine the depth map usingend-user-provided depth data (e.g., ordinal pairs, such as a pair ofclicks or screen taps on an image).

For example, a client device can display an image and a user can select(e.g., screen-tap, click, or specify image coordinates for) a firstpoint in the image, followed by a second point in the image. The activedepth detection system generates a vector from the first point to thesecond point to indicate a depth detection (e.g., to indicate that thefirst point corresponds to a part of the image that is closer to theviewer than the part of the image that corresponds to the second point,or vice-versa). The vector can be input into the neural network systemwhich has been trained to update a depth map using the ordinal pair(e.g., the pair of clicks) as constraints.

In some example embodiments, the active depth detection system istrained using end-to-end training techniques (e.g., back propagation).After the model is trained, it can be downloaded for use by differentusers as part of a user application (e.g., a messaging clientapplication 104 discussed below). When the user generates an image, thesystem can first generate an initial depth map using a base network,such as a Fully Convolutional Residual Neural Network (FCRN). The activedepth detection system can then refine the initial depth map usingordinal constraints (e.g., pairs of clicks) that indicate depthdirections of imaged regions (e.g., regions in the original image;regions in the initial depth map). In some example embodiments, theactive depth detection system implements a recurrent neural network thatis configured as an Alternating Direction Method of Multipliers (ADMM)module with multiple layers. The recurrent neural network can beexecuted over multiple iterations to generate variables processed in thedifferent layers. The output of the recurrent neural network is arefined depth map that can be used for further processing, such as imagemanipulation. Although in the following examples, the refined depth mapis used for image processing, other uses are possible. For example, theactive depth detection system can be implemented in an augmented realitysystem to more accurately generate simulations that modify theappearance of a user's surrounding environment. As an additionalexample; the active depth detection system can be implemented as part ofan autonomous vehicle vision system to ascertain relative depths ofobjects depicted in one or more images.

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple client devices 102, each ofwhich hosts a number of applications including a messaging clientapplication 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via a network 106(e.g., the Internet). In various embodiments, virtual machine learningcan be used by the messaging client application 104 and/or an imageprocessing system 116 to analyze images sent within the messaging system100 and to use this analysis to provide features within the messagingsystem 100.

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,include functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video, or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or themessaging server system 108, it will be appreciated that the location ofcertain functionality within either the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, but to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include message content, client device information, geolocationinformation, media annotation and overlays, message content persistenceconditions, image search data, social network information, and liveevent information, as examples, some of which rely on informationgenerated by analyzing images sent through the messaging system 100.Data exchanges within the messaging system 100 are invoked andcontrolled through functions available via user interfaces (UIs) of themessaging client application 104.

Turning now specifically to the messaging server system 108, anapplication programming interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112. Insome embodiments, the database 120 may also store results of imageprocessing or details of various trained and untrained support vectormachines that may be used by the messaging server system 108.

The API server 110 receives and transmits message data (e.g., commandsand message payloads) between the client device 102 and the applicationserver 112. Specifically, the API server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The API server 110 exposes variousfunctions supported by the application server 112, including accountregistration; login functionality; the sending of messages, via theapplication server 112, from a particular messaging client application104 to another messaging client application 104; the sending of mediafiles (e.g., images or video) from a messaging client application 104 toa messaging server application 114 for possible access by anothermessaging client application 104; the setting of a collection of mediadata (e.g., a story); the retrieval of such collections: the retrievalof a list of friends of a user of a client device 102; the retrieval ofmessages and content; the addition and deletion of friends to and from asocial graph; the location of friends within the social graph; andapplication events (e.g., relating to the messaging client application104).

The application server 112 hosts a number of applications andsubsystems, including the messaging server application 114, the imageprocessing system 116, a social network system 122, and an active depthsystem 150, according to some example embodiments. The messaging serverapplication 114 implements a number of message processing technologiesand functions, particularly related to the aggregation and otherprocessing of content (e.g., textual and multimedia content) included inmessages received from multiple instances of the messaging clientapplication 104. As will be described in further detail, the text andmedia content from multiple sources may be aggregated into collectionsof content (e.g., called stories or galleries). These collections arethen made available, by the messaging server application 114, to themessaging client application 104. Other processor- and memory-intensiveprocessing of data may also be performed server-side by the messagingserver application 114, in view of the hardware requirements for suchprocessing.

The application server 112 also includes the image processing system116, which is dedicated to performing various image processingoperations, typically with respect to images or video received withinthe payload of a message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph (e.g., an entity graph304 in FIG. 3) within the database 120. Examples of functions andservices supported by the social network system 122 include theidentification of other users of the messaging system 100 with whom aparticular user has relationships or whom the particular user is“following,” and also the identification of other entities and interestsof a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of subsystems, namely an ephemeral timer system 202, a collectionmanagement system 204, an annotation system 206, and the active depthsystem 150.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message orcollection of messages (e.g., a story), selectively display and enableaccess to messages and associated content via the messaging clientapplication 104. Further details regarding the operation of theephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image, video, and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text, and audio) may be organized into an“event gallery” or an “event story.” Such a collection may be madeavailable for a specified time period, such as the duration of an eventto which the content relates. For example, content relating to a musicconcert may be made available as a “story” for the duration of thatmusic concert. The collection management system 204 may also beresponsible for publishing an icon that provides notification of theexistence of a particular collection to the user interface of themessaging client application 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content in a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay (e.g., a geofilter or filter) tothe messaging client application 104 based on a geolocation of theclient device 102. In another example, the annotation system 206operatively supplies a media overlay to the messaging client application104 based on other information, such as social network information ofthe user of the client device 102. A media overlay may include audio andvisual content and visual effects. Examples of audio and visual contentinclude pictures, texts, logos, animations, and sound effects. Anexample of a visual effect includes color overlaying. The audio andvisual content or the visual effects can be applied to a media contentitem (e.g., a photo) at the client device 102. For example, the mediaoverlay includes text that can be overlaid on top of a photographgenerated by the client device 102. In another example, the mediaoverlay includes an identification of a location (e.g., Venice Beach), aname of a live event, or a name of a merchant (e.g., Beach CoffeeHouse). In another example, the annotation system 206 uses thegeolocation of the client device 102 to identify a media overlay thatincludes the name of a merchant at the geolocation of the client device102. The media overlay may include other indicia associated with themerchant. The media overlays may be stored in the database 120 andaccessed through the database server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map and upload content associated with the selectedgeolocation. The user may also specify circumstances under whichparticular content should be offered to other users. The annotationsystem 206 generates a media overlay that includes the uploaded contentand associates the uploaded content with the selected geolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 206 associates the mediaoverlay of a highest-bidding merchant with a corresponding geolocationfor a predefined amount of time

FIG. 3 is a schematic diagram illustrating data 300, which may be storedin the database 120 of the messaging server system 108, according tocertain example embodiments. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table314. An entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, and so forth. Regardless of type, any entity regardingwhich the messaging server system 108 stores data may be a recognizedentity. Each entity is provided with a unique identifier, as well as anentity type identifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between or among entities. Merely forexample, such relationships may be social, professional (e.g., work at acommon corporation or organization), interest-based, or activity-based.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters) which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a Global Positioning System(GPS) unit of the client device 102. Another type of filter is a datafilter, which may be selectively presented to a sending user by themessaging client application 104, based on other inputs or informationgathered by the client device 102 during the message creation process.Examples of data filters include a current temperature at a specificlocation, a current speed at which a sending user is traveling, abattery life for a client device 102, or the current time.

Other annotation data that may be stored within the annotation table isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe message table 314. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., any user forwhom a record is maintained in the entity table 302). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client application 104 may include an icon that isuser-selectable to enable a sending user to add specific content to hisor her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices 102 havelocation services enabled and are at a common location or event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104, based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text, to be generated by a user via        a user interface of the client device 102 and that is included        in the message 400.    -   A message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102, and that is included in the message 400.    -   A message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400.    -   A message audio payload 410: audio data, captured by a        microphone or retrieved from the memory component of the client        device 102, and that is included in the message 400.    -   Message annotations 412: annotation data (e.g., filters,        stickers, or other enhancements) that represents annotations to        be applied to the message image payload 406, message video        payload 408, or message audio payload 410 of the message 400.    -   A message duration parameter 414: a parameter value indicating,        in seconds, the amount of time for which content of the message        400 (e.g., the message image payload 406, message video payload        408, and message audio payload 410) is to be presented or made        accessible to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message 400. Multiple message geolocation        parameter 416 values may be included in the payload, with each        of these parameter values being associated with respective        content items included in the content (e.g., a specific image in        the message image payload 406, or a specific video in the        message video payload 408).    -   A message story identifier 418: identifier values identifying        one or more content collections (e.g., “stories”) with which a        particular content item in the message image payload 406 of the        message 400 is associated. For example, multiple images within        the message image payload 406 may each be associated with        multiple content collections using identifier values.    -   A message tag 420: one or more tags, each of which is indicative        of the subject matter of content included in the message        payload. For example, where a particular image included in the        message image payload 406 depicts an animal (e.g., a lion), a        tag value may be included within the message tag 420 that is        indicative of the relevant animal. Tag values may be generated        manually, based on user input, or may be automatically generated        using, for example, image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 400 was generated and from which the message        400 was sent.    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of the message 400may be pointers to locations in tables within which content data valuesare stored. For example, an image value in the message image payload 406may be a pointer to (or address of) a location within the image table308. Similarly, values within the message video payload 408 may point todata stored within the video table 310, values stored within the messageannotations 412 may, point to data stored in the annotation table 312,values stored within the message story identifier 418 may point to datastored in the story table 306, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within the entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message story 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone embodiment, where the messaging client application 104 is anapplication client, an ephemeral message 502 is viewable by a receivinguser for up to a maximum of 10 seconds, depending on the amount of timethat the sending user specifies using the message duration parameter506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message story 504 (e.g., a personal story, or an event story).The ephemeral message story 504 has an associated story durationparameter 508, a value of which determines a time duration for which theephemeral message story 504 is presented and accessible to users of themessaging system 100. The story duration parameter 508, for example, maybe the duration of a music concert, where the ephemeral message story504 is a collection of content pertaining to that concert.Alternatively, a user (either the owning user or a curator user) mayspecify the value for the story duration parameter 508 when performingthe setup and creation of the ephemeral message story 504.

Additionally, each ephemeral message 502 within the ephemeral messagestory 504 has an associated story participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message story504. Accordingly, a particular ephemeral message 502 may “expire” andbecome inaccessible within the context of the ephemeral message story504, prior to the ephemeral message story 504 itself expiring in termsof the story duration parameter 508. The story duration parameter 508,story participation parameter 510, and message receiver identifier 424each provide input to a story timer 514, which operationally determineswhether a particular ephemeral message 502 of the ephemeral messagestory 504 will be displayed to a particular receiving user and, if so,for how long. Note that the ephemeral message story 504 is also aware ofthe identity of the particular receiving user as a result of the messagereceiver identifier 424.

Accordingly, the story timer 514 operationally controls the overalllifespan of an associated ephemeral message story 504, as well as anindividual ephemeral message 502 included in the ephemeral message story504. In one embodiment, each and every ephemeral message 502 within theephemeral message story 504 remains viewable and accessible for a timeperiod specified by the story duration parameter 508. In a furtherembodiment, a certain ephemeral message 502 may expire, within thecontext of the ephemeral message story 504, based on a storyparticipation parameter 510. Note that a message duration parameter 506may still determine the duration of time for which a particularephemeral message 502 is displayed to a receiving user, even within thecontext of the ephemeral message story 504. Accordingly, the messageduration parameter 506 determines the duration of time that a particularephemeral message 502 is displayed to a receiving user, regardless ofwhether the receiving user is viewing that ephemeral message 502 insideor outside the context of an ephemeral message story 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message story 504based on a determination that it has exceeded an associated storyparticipation parameter 510. For example, when a sending user hasestablished a story participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message story 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message story 504 either when the storyparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message story 504 has expired, or when theephemeral message story 504 itself has expired in terms of the storyduration parameter 508.

In certain use cases, a creator of a particular ephemeral message story504 may specify an indefinite story duration parameter 508. In thiscase, the expiration of the story participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message story504 will determine when the ephemeral message story 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage story 504, with a new story participation parameter 510,effectively extends the life of an ephemeral message story 504 to equalthe value of the story participation parameter 510.

In response to the ephemeral timer system 202 determining that anephemeral message story 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (e.g., specifically, the messaging client application 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage story 504 to no longer be displayed within a user interface ofthe messaging client application 104. Similarly, when the ephemeraltimer system 202 determines that the message duration parameter 506 fora particular ephemeral message 502 has expired, the ephemeral timersystem 202 causes the messaging client application 104 to no longerdisplay an indicium (e.g., an icon or textual identification) associatedwith the ephemeral message 502.

FIG. 6 shows example functional engines of an active depth system 150,according to some example embodiments. As illustrated, the active depthsystem 150 comprises an interface engine 600, a training engine 605, adepth engine 610, and a content engine 615. The interface engine 600manages communications with the messaging server application 114 togenerate user interfaces, receive input data (e.g., click pairs,selection of a button), and generate content (e.g., images, video). Thetraining engine 605 is configured to train the model implemented in thedepth engine 610. The depth engine 610 is configured to generate a depthmap from an individual image using the image and one or more ordinalconstraints (e.g., click pairs) input by a user. The content engine 615is configured to perform one or more actions using the depth mapgenerated by the depth engine 610. For example, the content engine 615can be configured to apply an image effect to an image using depthinformation generated by the depth engine 610, and/or overlay one ormore items of content on the image.

FIG. 7 shows a flow diagram of a method 700 for implementing an activedepth map, according to some example embodiments. At operation 705, thetraining engine 605 trains an active depth system model, such as anetwork 900, discussed below with reference to FIG. 9A. Because thenetwork 900 is fully differentiable, the network 900 can be trainedend-to-end using gradient descent. At operation 710, the interfaceengine 600 generates an image. For example, the interface engine 600generates an image using an image sensor of the client device 102. Atoperation 715, the depth engine 610 receives user interaction data. Forexample, at operation 715, the depth engine 610 receives click pair dataas a sequence of screen taps on the image depicted on a display deviceof the client device 102. At operation 720, the depth engine 610generates a refined depth map using the trained network 900, asdiscussed in further detail below with reference to FIGS. 9A and 9B. Atoperation 730, the content engine 615 modifies the image using therefined depth map that is generated by the depth engine 610. Forexample, at operation 730, the content engine 615 removes a backgroundarea of the generated image using the refined depth map generated atoperation 720. At operation 735, the content engine 615 transmit themodified image as an ephemeral message (e.g., an ephemeral message 502)to a network site (e.g., a social media network site) for access byother network site users.

FIG. 8 shows a flow diagram of an example method 800 for utilizing setsof click pair data, according to some example embodiments. In someimplementations, the user of the client device 102 inputs multiple pairsof clicks, each click pair set indicating a depth direction of a regionin an image. The depth engine 610 can implement the method 800 as asubroutine of operation 720, in which a refined depth map is generated.

At operation 805, the depth engine 610 identifies an ordinal pair, suchas a pair of clicks input by a user on an image displayed on a clientdevice. At operation 810, the depth engine 610 generates a base depthmap for refinement. For example, at operation 810, the depth engine 610implements an FCRN to generate an initial depth map from an image (e.g.,the image generated at operation 710, FIG. 7).

At operation 815, the depth engine 610 uses the received ordinal pair tofurther refine the base depth map. For example, at operation 815, thedepth engine 610 runs an ADMM module one or more iterations to refineregions of the initial depth map. At operation 820, the depth engine 610determines whether there are additional sets of ordinal pairs. If theuser has input additional click pairs, then at operation 820 the method800 continues to operation 805, and the depth engine 610 further refinesthe depth map using the additional ordinal pair information inoperations 810 and 815. For example, a first ordinal pair may increasethe depth accuracy of a first region of a depth map (e.g., the lowerright corner), and a second ordinal pair may increase the depth accuracyof a second different region in the depth map (e.g., the upper left,corner), and so on. Alternatively, returning to operation 820, if′ thedepth engine 610 determines that the user has not input further ordinalpairs, the method 800 proceeds to operation 825, in which the depthengine 610 stores the refined depth map.

FIG. 9A shows an example network 900 for the depth engine 610, accordingto some example embodiments. As illustrated, an initial image 905 isinput into a base network 910 (e.g., a Fully Convolutional. ResidualNeural Network (FCRN)) that generates a base depth map 915. FIG. 9Bshows an example image 950 and depth map 955. The depth map 955indicates the depth of different areas in the image 950 using datavalues, such as lightness and darkness. For instance, the pixels of thepool table are darker than the pixels of the wall in the depth map 955,which indicates that the pool table is closer to the viewer (e.g., user,camera lens) than the wall depicted behind the pool table. The depth mapcan be a separate file from its corresponding image, but can also beintegrated into the image as extra channel data for each pixel.

Returning to FIG. 9A, the base depth map 915 is input into an ADMMmodule 925, which operates in several iterations (e.g., iteration 1,iteration 2, . . . iteration n) to generate a refined depth map 930.Further input into the ADMM module 925 is pair data 920, which comprisespairs of points or clicks which specify relative orders between pairs ofpixels in a depth direction, as illustrated in FIG. 11, discussed below.

In some example embodiments, the ADMM module 925 is implemented as arecurrent neural network that implements update rules to generate therefined depth map 930. The pair data 920 comprises user input guidance(e.g., clicks to indicate depth directions), which is used as ordinalconstraints on the inferred depth estimations. The depth estimation canbe modeled as a quadratic programming scheme with linear constraints. Inparticular, let N be the total number of pixels in an image, and let xand y be the vector representations for the input image and refineddepth (respectively) to be solved. The refined depth values y arebounded within a range [0, D]. Given M pairs of ordinal constraints fromuser guidance (user click pairs), the objective function for optimizingy is:

$\begin{matrix}{y^{*} = {{{argmin}_{y}{f_{u}\left( {y,x} \right)}} + {\sum\limits_{\alpha}{f_{p}\left( {y_{\alpha},x} \right)}}}} & \lbrack 1\rbrack \\{s.t.} & \; \\{{Ay} \leq B} & \; \\{{{{where}\mspace{14mu} A} = \begin{bmatrix}{- I} \\I \\P\end{bmatrix}},{B = \begin{bmatrix}0 \\{D\; 1} \\0\end{bmatrix}},} & \;\end{matrix}$

I is the identity matrix, and 0 and 1 are vectors of all 0s and 1s.Furthermore, f_(u)(y, x) is a unary potential encoding the predictionfrom a base deep neural network, and f_(p) (y_(α), x) is a high-orderpotential encoding the spatial relationship between neighboring pixels.Ay≤B encodes the hard constraints for ordinal relations. The first twoparts in A and B ensure that the refined depth output is within thevalid range [0, D]. P is an M×N matrix encoding M different ordinalconstraints. We use P_(kj)=1 and P_(kj′)=−1 if (j, j′) is an ordinalpair where k≤M.

The unary potentials f_(u) are of the form f_(u)(y, x; w)=½ ∥y−h(x; w)∥²which measures the L2 distance between y and h(x; w). For estimatingdepths, h(x; w) indicates the output from a base depth predictionnetwork (e.g., the base network 910) parameterized by network weights w.Minimizing the unary terms is equivalent to minimizing the mean squarederror between refined depths and base network outputs.

The high-order potentials f_(p) are of the form f_(p)(y_(α), x;w)=h_(α)(x; w)g_(α)(W_(α) y). Here W_(α) denotes a transformation matrixfor a filtering operation, and h_(α)(x; w) provides per-pixel guidanceinformation that places stronger local smoothness for pixels onlow-frequency edges. The h_(α)(x; w) is constant for all the pixels toshow improvement from ordinal constraints.

To solve for refined depth values y, the ADMM algorithm is implementedto handle non-differentiable objectives and hard constraints whilemaintaining fast convergence. Equation 1 is reconfigured using auxiliaryvariables z={z₁, . . . , z_(A)}. In particular:

$y^{*} = {{{\arg \min}_{y}\frac{1}{2}{{y - {h\left( {x;w} \right)}}}_{2}^{2}} + {\sum\limits_{\alpha}{{h_{\alpha}\left( {x;w} \right)}{g_{\alpha}\left( z_{\alpha} \right)}}}}$s.t.Ay ≤ B W_(α)y = z_(α), z_(α) ∈ z

The augmented Lagrangian of the original objective function is then:

${L_{\rho}\left( {x,y,z,\lambda,\xi} \right)} = {{\frac{1}{2}{{y - {h\left( {x;w} \right)}}}_{2}^{2}} + {\sum\limits_{\alpha}{{h_{\alpha}\left( {x;w} \right)}{g_{\alpha}\left( z_{\alpha} \right)}}} + {\sum\limits_{\alpha}{\frac{\rho_{\alpha}}{2}{{{W_{\alpha}y} - z_{\alpha}}}_{2}^{2}}} + {\lambda^{T}\left( {{Ay} - B} \right)} + {\sum\limits_{\alpha}{\xi_{\alpha}^{T}\left( {{W_{\alpha}y} - z_{\alpha}} \right)}}}$

where ρ_(α) is a constant penalty hyperparameter, and λ, ξ are Lagrangemultipliers with λ≥0. The variables y, z, λ, ξ are solved by alternatingbetween the following subproblems.

To solve for refined depth y: the y update rule is the derivative of theLagrangian function with respect to y:

$\overset{˜}{y} = {{{{\arg \min}_{y}\frac{1}{2}{{y - {h\left( {x;w} \right)}}}_{2}^{2}} + {\sum\limits_{\alpha}{\frac{\rho_{\alpha}}{2}{{{W_{\alpha}y} - z_{\alpha}}}_{2}^{2}}} + {\lambda^{T}\left( {{Ay} - B} \right)} + {\sum\limits_{\alpha}{\xi_{\alpha}^{T}\left( {{W_{\alpha}y} - z_{\alpha}} \right)}}} = {\left( {I + {\sum\limits_{\alpha}{\rho_{\alpha}W_{\alpha}^{T}W_{\alpha}}}} \right)^{- 1}\left( {{h\left( {x;w} \right)} - {A^{T}\lambda} + {\sum\limits_{\alpha}{W_{\alpha}^{T}\left( {\rho_{\alpha^{Z_{\alpha} -}}\xi_{\alpha}} \right)}}} \right)}}$

This step uses the term A^(T) λ to encode the ordinal constraints andadjust the outputs from the base network. The depths are refinediteratively in a forward pass through the ADMM network modules.

To solve for auxiliary variables z: let g_(α)(⋅)=∥⋅∥₁ be the L1smoothness priors on y and S(a, b) be the soft thresholding function.The z update rules are obtained by solving a Lasso problem:

$\overset{˜}{z} = {{{argmin}_{\{ z_{\alpha}\}}{h_{\alpha}\left( {x;w} \right)}{q_{\alpha}\left( z_{\alpha} \right)}} + {\frac{\rho_{\alpha}}{2}{{{W_{\alpha}y} - z_{\alpha}}}_{2}^{2}} + {\sum\limits_{\alpha}{\xi_{\alpha}^{T}\left( {{W_{\alpha}y} - z_{\alpha}} \right)}}}$

And for each z_(α):

${\overset{˜}{z}}_{\alpha} = {{{{argmin}_{\{ z_{\alpha}\}}{h_{\alpha}\left( {x;w} \right)}{g_{\alpha}\left( z_{\alpha} \right)}} + {\frac{\rho_{\alpha}}{2}{{{W_{\alpha}y} - z_{\alpha}}}_{2}^{2}} + {\xi_{\alpha}^{T}\left( {{W_{\alpha}y} - z_{\alpha}} \right)}} = {S\left( {{{W_{\alpha}y} + \frac{\xi_{\alpha}}{p_{\alpha}}},\frac{h_{\alpha}\left( {x;w} \right)}{\rho_{\alpha}}} \right)}}$

To solve for Lagrangian multipliers λ and ξ, the update rule for λ isobtained using gradient ascent:

$\begin{matrix}{\overset{˜}{\lambda} = {\max \left( {{{argmin}_{\{\lambda\}}{\lambda \left( {{Ay} - B} \right)}},0} \right)}} \\{= {\max \left( {{\lambda + {\eta \left( {{Ay} - B} \right)}},0} \right)}}\end{matrix}\quad$

Similarly, for each ξ_(α), we have the gradient ascent update rule:

$\begin{matrix}{\overset{˜}{\xi} = {{argmin}_{\xi_{\alpha}}{\xi_{\alpha}^{T}\left( {{W_{\alpha}y^{(n)}} - z_{\alpha}} \right)}}} \\{= {\xi_{\alpha} + {\tau_{\alpha}\left( {{W_{\alpha}y} - z_{\alpha}} \right)}}}\end{matrix}\quad$

where η and τ_(α) are the hyperparameters denoting gradient update stepsizes.

In some example embodiments, the ADMM module 925 is iterative in nature,weights are not shared, and the number of iterations is fixed to allowthe ADMM module 925 to use convolutional neural networks with customizedactivation functions.

A call-out 933 shows different layers of the ADMM module 925, accordingto some example embodiments. The ADMM module 925 is configured to run aniteration of the above update rules, according to some exampleembodiments. The filters to encode the transformation W_(α) are learnedvia back propagation training. The data tensors z_(α), ξ_(α), and λ areinitialized as zeros.

In some example embodiments, the depth engine 610 uses five ADMMmodules, which corresponds to running the ADMM module 925 for fiveiterations. Each ADMM instance contains 64 transformations W_(α) (e.g.,each convolution layer includes 64 filters, each deconvolution layerincludes 64 layers, etc.). In some example embodiments, since alloperations in the ADMM module 925 are differentiable, the entire network900 (e.g., the base network 910 and ADMM module 925) can be learnedend-to-end using gradient descent. In some example embodiments, thenetwork 900 implements a standard mean squared error (MSE) as the lossfunction.

A first layer 935A in the ADMM module 925 is configured to solve forrefined depth y. Calculating the numerator corresponds to applying adeconvolution 937 (e.g., transposed convolution) step on eachρ_(α)z_(α)−ξ_(α) and taking the sum of results together. Calculating thedenominator is performed by converting the deconvolution kernels tooptical transfer functions and taking the sum. Calculating the finaloutput is performed by first applying a fast Fourier transform (FFT) onthe numerator followed by an inverse FFT on the division result.

A second layer 935B in the ADMM module 925 solves for auxiliaryvariables z. This can be done with a convolution layer 945 on y usingthe same filters shared with the deconvolution layer. The convolutionlayer output is passed (as indicated by pass operator 940) through anon-linear activation layer 943 that implements a standard softthresholding function S(a, h). In practice, we implement this softthresholding function using two rectified linear unit (ReLU) functions:S(a, b)=ReLU(a−b)−ReLU(−α−b). In some example embodiments, theconvolution layer 945 does not share weights with the deconvolutionlayer in order to increase network capacity.

A third layer 935C and fourth layer 935D in the ADMM module 925correspond to gradient ascent steps that solve for Lagrange multipliersλ and ξ, respectively. These steps are implemented as tensor subtractionand summation operations. The updated result of λ after gradient ascentis passed through an activation layer 939 (e.g., an additional ReLUlayer) to satisfy the non-negative constraint on λ.

FIGS. 10-13 show example user interfaces for implementing the activedepth system 150, according to some example embodiments. As illustratedin FIG. 10, a user 1000 is holding a client device 102 which displays auser interface 1010. The user 1000 has generated an image 1015 using acapture image button 1005. To input click pair data, the user 1000selects an add points button 1020. Turning to FIG. 11, the user 1000 hasselected the add points button 1020 and screen tapped twice to createone click pair set. In particular, the user 1000 has screen tapped at apoint 1100 followed by screen tapping at a point 1105 to indicate thatthe pixel corresponding to the point 1100 is closer than the pixelcorresponding to the point 1105. Although in the examples discussed thefirst point is closer than the second point, it is to be appreciatedthat the ordinal pair data can be configured in the reverse direction(e.g., the first point indicates that that point is farther away than asubsequent second point). The active depth system 150 receives thescreen taps and generates an arrow connecting the two points 1100 and1105 to indicate that the direction of depth for that area of the image(e.g., the depicted ceiling above the cashier) is in the direction ofthe arrow created by the two points. While the ordinal pair in theexamples discussed here is generated by a pair of clicks provided by theuser, it is to be appreciated that ordinal pair data can be generated inother ways, such as by swipe gestures or by inputting coordinates of afirst point and a second point into text input fields.

In response to receiving a click pair (e.g., the points 1100 and 1105),the depth engine 610 generates a base depth map of the image 1015 andfurther uses the click pair to generate and store a refined depth mapusing the click pair, as discussed above. Turning to FIG. 12, thecontent engine 615 can be configured to generate a modified image 1200from the image 1015 using the generated refined depth map. Inparticular, the content engine 615 uses the refined depth map toidentify background areas of the image 1015 and remove the backgroundareas to generate the modified image 1200. Turning to FIG. 13, thecontent engine 615 can be further configured to overlay additionalcontent, such as location content 1300, on the modified image 1200. Theuser 1000 can then select a post button 1305 to publish the modifiedimage 1200 with the overlay content (e.g., the location content 1300) asan ephemeral message on a network site.

FIG. 14 is a block diagram illustrating an example software architecture1406, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 14 is a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1406 may execute on hardwaresuch as a machine 1500 of FIG. 15 that includes, among other things,processors 1510, memory 1530, and I/O components 1550. A representativehardware layer 1452 is illustrated and can represent, for example, themachine 1500 of FIG. 15. The representative hardware layer 1452 includesa processing unit 1454 having associated executable instructions 1404.The executable instructions 1404 represent the executable instructionsof the software architecture 1406, including implementation of themethods, components, and so forth described herein. The hardware layer1452 also includes memory and/or storage modules 1456, which also havethe executable instructions 1404. The hardware layer 1452 may alsocomprise other hardware 1458.

In the example architecture of FIG. 14, the software architecture 1406may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1406may include layers such as an operating system 1402, libraries 1420,frameworks/middleware 1410, applications 1416, and a presentation layer1414. Operationally, the applications 1416 and/or other componentswithin the layers may invoke API calls 1408 through the software stackand receive a response in the form of messages 1412. The layersillustrated are representative in nature, and not all softwarearchitectures have all layers. For example, some mobile orspecial-purpose operating systems may not provide aframeworks/middleware 1410, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1402 may manage hardware resources and providecommon services. The operating system 1402 may include, for example, akernel 1422, services 1424, and drivers 1426. The kernel 1422 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1422 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1424 may provideother common services for the other software layers. The drivers 1426are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1426 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 1420 provide a common infrastructure that is used by theapplications 1416 and/or other components and/or layers. The libraries1420 provide functionality that allows other software components toperform tasks in an easier fashion than by interfacing directly with theunderlying operating system 1402 functionality (e.g., kernel 1422,services 1424, and/or drivers 1426). The libraries 1420 may includesystem libraries 1444 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1420 may include API libraries 1446 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, or PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphic content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 1420 may also include a wide variety ofother libraries 1448 to provide many other APIs to the applications 1416and other software components/modules.

The frameworks/middleware 1410 provide a higher-level commoninfrastructure that may be used by the applications 1416 and/or othersoftware components/modules. For example, the frameworks/middleware 1410may provide various graphic user interface (GUI) functions, high-levelresource management, high-level location services, and so forth. Theframeworks/middleware 1410 may, provide a broad spectrum of other APIsthat may be utilized by the applications 1416 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system 1402 or platform.

The applications 1416 include built-in applications 1438 and/orthird-party applications 1440. Examples of representative built-inapplications 1438 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 1440 may includean application developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1440 may invoke the API calls 1406 provided bythe mobile operating system (such as the operating system 1402) tofacilitate functionality described herein.

The applications 1416 may use built-in operating system functions (e.g.,kernel 1422, services 1424, and/or drivers 1426), libraries 1420, andframeworks/middleware 1410 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systemsinteractions with a user may occur through a presentation layer, such asthe presentation layer 1414. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 15 is a block diagram illustrating components of a machine 1500,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 15 shows a diagrammatic representation of the machine1500 in the example form of a computer system, within which instructions1516 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1500 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1516 may be used to implement modules or componentsdescribed herein. The instructions 1516 transform the general,non-programmed machine 1500 into a particular machine 1500 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1500 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1500 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1500 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1516, sequentially or otherwise, that specify actions to betaken by the machine 1500. Further, while only a single machine 1500 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1516 to perform any one or more of the methodologiesdiscussed herein.

The machine 1500 may include processors 1510 having individualprocessors 1512 and 1514 (e.g., cores), memory/storage 1530, and I/Ocomponents 1550, which may be configured to communicate with each othersuch as via a bus 1502. The memory/storage 1530 may include a memory1532, such as a main memory, or other memory storage, and a storage unit1536, both accessible to the processors 1510 such as via the bus 1502.The storage unit 1536 and memory 1532 store the instructions 1516embodying any one or more of the methodologies or functions describedherein. The instructions 1516 may also reside, completely or partially,within the memory 1532, within the storage unit 1536, within at leastone of the processors 1510 (e.g., within the processor's cache memory),or any suitable combination thereof, during execution thereof by themachine 1500. Accordingly, the memory 1532, the storage unit 1536, andthe memory of the processors 1510 are examples of machine-readablemedia.

The I/O components 1550 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1550 that are included in a particular machine 1500 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1550 may include many other components that are not shown inFIG. 15. The I/O components 1550 are grouped according to functionalitymerely for simplifying the following discussion, and the grouping is inno way limiting. In various example embodiments, the I/O components 1550may include output components 1552 and input components 1554. The outputcomponents 1552 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid-crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1554 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1550 may includebiometric components 1556, motion components 1558, environmentcomponents 1560, or position components 1562 among a wide array of othercomponents. For example, the biometric components 1556 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion components 1558 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1560 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gassensors to detect concentrations of hazardous gases for safety or tomeasure pollutants in the atmosphere), or other components that mayprovide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1562 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1550 may include communication components 1564operable to couple the machine 1500 to a network 1580 or devices 1570via a coupling 1582 and a coupling 1572, respectively. For example, thecommunication components 1564 may include a network interface componentor other suitable device to interface with the network 1580. In furtherexamples, the communication components 1564 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1570 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1564 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1564 may include radio frequency identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional barcodes such as Universal Product Code (UPC) barcode,multi-dimensional barcodes such as Quick Response (QR) code, Aztec code,Data Matrix, Dataglyph, MaxiCode, PDF415, Ultra Code, UCC RSS-2Dbarcode, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1564, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation; location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine, and includes digital or analog communications signals orother intangible media to facilitate communication of such instructions.Instructions may be transmitted or received over the network using atransmission medium via a network interface device and using any one ofa number of well-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine that interfaces toa communications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, PDA, smartphone,tablet, ultrabook, netbook, multi-processor system, microprocessor-basedor programmable consumer electronics system, game console, set-top box,or any other communication device that a user may use to access anetwork.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network andthe coupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or anothertype of cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (CPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation. Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long-Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data transfer technology.

“EPHEMERAL MESSAGE” in this context refers to a message that isaccessible for a time-limited duration. An ephemeral message 502 may bea text, an image, a video, and the like. The access time for theephemeral message 502 may be set by the message sender. Alternatively,the access time may be a default setting or a setting specified by therecipient. Regardless of the setting technique, the message istransitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, adevice, or other tangible media able to store instructions and datatemporarily or permanently and may include, but is not limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., erasable programmable read-only memory (EPROM)), and/orany suitable combination thereof. The term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term “machine-readable medium” shallalso be taken to include any medium, or combination of multiple media,that is capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described herein. Accordingly, a “machine-readablemedium” refers to a single storage apparatus or device, as well as“cloud-based” storage systems or storage networks that include multiplestorage apparatus or devices. The term “machine-readable medium”excludes signals per se.

“COMPONENT” in this context refers to a device, a physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner.

In various example embodiments, one or more computer systems (e.g., astandalone computer system, a client computer system, or a servercomputer system) or one or more hardware components of a computer system(e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwarecomponent that operates to perform certain operations as describedherein. A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC). A hardware component may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors.

It will be appreciated that the decision to implement a hardwarecomponent mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.Considering embodiments in which hardware components are temporarily,configured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between or among suchhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output.

Hardware components may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented components.

Moreover, the one or more processors may also operate to supportperformance of the relevant operations in a “cloud computing”environment or as a “software as a service” (SaaS). For example, atleast some of the operations may be performed by a group of computers(as examples of machines including processors), with these operationsbeing accessible via a network (e.g., the Internet) and via one or moreappropriate interfaces (e.g., an API). The performance of certain of theoperations may be distributed among the processors, not only residingwithin a single machine, but deployed across a number of machines. Insome example embodiments, the processors or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexample embodiments, the processors or processor-implemented componentsmay be distributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,” etc.) and which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction. Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-FrequencyIntegrated Circuit (RFIC), or any combination thereof. A processor mayfurther be a multi-core processor having two or more independentprocessors (sometimes referred to as “cores”) that may executeinstructions contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

What is claimed is:
 1. A method comprising: storing, on a user device, afirst neural network and a second neural network, the first neuralnetwork in use outputting into the second neural network; identifying,on the user device, an image depicting an environment; receiving, by theuser device, an ordinal pair indicating a direction of depth in theenvironment depicted in the image; generating, on the user device, aninitial depth map from the image using the first neural network;generating, on the user device, an updated depth map by inputting thereceived ordinal pair and the initial depth map into the second neuralnetwork; and storing the updated depth map.
 2. The method of claim 1,further comprising: generating a modified image by modifying the imageusing the updated depth map.
 3. The method of claim 1 wherein the secondneural nets is a recurrent neural network.
 4. The method of claim 3,wherein the recurrent neural network is trained to implement analternating direction method of multipliers scheme.
 5. The method ofclaim 3, wherein the first neural network is a trained neural networkand the second neural network is configured to receive the ordinal pairas constraints after training of the trained neural network.
 6. Themethod of claim 1, further comprising: receiving, by the user device, atleast one additional ordinal pair, each additional ordinal pairindicating an additional direction of depth in the environment depictedin the image.
 7. The method of claim 1, further comprising: generating,on the user device, the image using an image sensor of the user device;and displaying the generated image on a display device of the userdevice.
 8. The method of claim 7, wherein receiving the ordinal paircomprises receiving a first point on the image and a second point on theimage while the generated image is displayed on the display device. 9.The method of claim 2, further comprising: identifying, using theupdated depth map, a background area of the image.
 10. The method ofclaim 9, wherein the modified image is generated by applying an imageeffect to the background area of the image.
 11. A system comprising: oneor more processors of a machine; and a memory storing instructions that,when executed by at least one processor among the one or moreprocessors, cause the machine to perform operations comprising: storing,in the memory, a first neural network and a second neural network, thefirst neural network in use outputting into the second neural network;identifying, in the memory, an image depicting an environment; receivingan ordinal pair indicating a direction of depth in the environmentdepicted in the image; generating an initial depth map from the imageusing the first neural network; generating an updated depth map byinputting the received ordinal pair and the initial depth map into thesecond neural network; and storing the updated depth map in the memory.12. The system of claim 11, wherein the operations further comprise:generating a modified image by modifying the image using the updateddepth map.
 13. The system of claim 11 wherein the first neural networkis a trained neural network and the second neural network is configuredto receive the ordinal pair as constraints after training of the trainedneural network.
 14. The system of claim 11, wherein the system comprisesa display device, and wherein receiving the ordinal pair comprisesreceiving a first point on the image and a second point on the imagewhile the image is displayed on the display device.
 15. A methodcomprising: identifying, on a user device, an image depicting anenvironment; receiving, by the user device, an ordinal pair indicating adirection of depth in the environment depicted in the image; generatinga depth map by inputting the received ordinal pair into a depth enginerunning on the user device; and storing the depth map.
 16. The method ofclaim 15, wherein the depth engine comprises a neural network.
 17. Themethod of claim 15, further comprising: generating a modified image bymodifying the image using the depth map; and displaying the modifiedimage on a display device of the user device.
 18. The method of claim15, further comprising: receiving; by the user device, at least oneadditional ordinal pair, each additional ordinal pair indicating anadditional direction of depth in the environment depicted in the image.19. The method of claim 15, further comprising: generating, on the userdevice, the image using an image sensor of the user device; anddisplaying the generated image on a display device of the user device,wherein receiving the ordinal pair comprises receiving a first point onthe image and a second point on the image while the image is displayedon the display device.
 20. The method of claim 15, further comprising:identifying, using the depth map, a background area of the image; andgenerating a modified image by applying an image effect to thebackground area of the image.